Amplify-and-forward cooperative OFDM with multiple-relays: performance analysis and relay selection methods
Bibliographic record
Abstract
In this paper, we investigate a cooperative system with multiple relays and amplify-and-forward relaying over frequency-selective channels. To extract the available multipath diversity, we employ orthogonal frequency division multiplexing (OFDM) with precoding. Through the derivation of pairwise error probability (PEP), we demonstrate that PEP is not a simple exponential function of the signal-to-noise ratio (SNR), but it includes a term that involves some power of logarithm of the SNR. If that term is ignored, the diversity order is given by the summation of the channel length in the direct link and the minimum of channel lengths in each relaying link confirmed by simulation results. Based on the PEP expression, we also propose two relay selection strategies; one is on a per-subcarrier basis and the other is on an all-subcarrier basis. Our simulation results indicate that both strategies result in performance improvements although the per-subcarrier method performs better.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".